Tailoring an interactive dialog application based on creator provided content

ABSTRACT

Implementations relate to executing a tailored version of a dynamic interactive dialog application, where the tailored version is tailored based on structured content that is specified by a creator of the tailored version. Executing the tailored version of the interactive dialog application can be in response to receiving, via an assistant interface of an assistant application, an invocation phrase assigned to the tailored version and/or other user interface input that identifies the tailored version. In some implementations, a tailored version of a dynamic interactive dialog application is executed with persona value(s) that are specified by a creator of the tailored version and/or that are predicted based on structured content and/or other input provided by the creator in creating the tailored version. In some implementations, structured content and/or other input provided by a creator in creating a tailored version of an interactive dialog application is utilized in indexing the tailored version.

BACKGROUND

An automated assistant (also known as “personal assistant”, “mobileassistant”, etc.) may be interacted with by a user via a variety ofclient devices, such as smart phones, tablet computers, wearabledevices, automobile systems, standalone personal assistant devices, andso forth. An automated assistant receives input from the user (e.g.,typed and/or spoken natural language input) and responds with responsivecontent (e.g., visual and/or audible natural language output). Anautomated assistant interacted with via a client device may beimplemented via the client device itself and/or via one or more remotecomputing devices that are in network communication with the clientdevice (e.g., computing device(s) in “the cloud”).

SUMMARY

This specification is directed generally to methods, apparatus, andcomputer readable media for executing a tailored version of a dynamicinteractive dialog application, where the tailored version is tailoredbased on structured content that is specified by a creator of thetailored version. Executing the tailored version of the interactivedialog application can be in response to receiving, via an assistantinterface of an assistant application, an invocation phrase assigned tothe tailored version and/or other user interface input that identifiesthe tailored version. Executing the tailored version can includegenerating multiple instances of user interface output for presentationvia the assistant interface. Each of the multiple instances of userinterface output is for a corresponding dialog turn during execution ofthe interactive dialog application and each of the multiple instances isgenerated through adaptation of the dynamic interactive dialogapplication using the structured content. For example, various variablesof the dynamic interactive dialog application can be populated withvalues that are based on the creator specified structured content,thereby adapting the interactive dialog application to the structuredcontent.

As described herein, multiple tailored versions of a dynamic interactivedialog application can be executed, where each of the tailored versionsis executed based on corresponding structured content specified by acorresponding creator. For example, in executing a first tailoredversion, first values that are based on first structured contentspecified by a first creator can be utilized for various variables ofthe dynamic interactive dialog application; in executing a secondtailored version, second values that are based on second structuredcontent specified by a second creator can be utilized various variablesof the dynamic interactive dialog application can be populated with;etc.

In these and other manners, the same fixed code can be executed for eachof multiple tailored versions—while adapting, in the execution of eachtailored version, only variables that are specified by the creator inthe structured content for that version, specified by other userinterface input of the creator in generating that version, and/orpredicted based on the structured content and/or other user interfaceinput for that version. This can lead to a reduction in computationalresources that are necessary to create an interactive dialogapplication. For example, a creator of a tailored version of a dynamicinteractive dialog application can utilize computational resources inspecifying variables through structured content and/or other input, andthose variables utilized to adapt the interactive dialog application asdescribed above (and elsewhere herein). However, the creator need notutilize significant computational resources in specifying various codefor full execution of the tailored version, as the fixed code of thedynamic interactive dialog application is instead utilized. Moreover,this can lead to reduction in the amount of computer hardware storagespace needed to store multiple applications. For example, the variablesfor each of multiple tailored versions can be stored—without requiring aunique instance of the fixed code to be stored for each of the multipletailored versions.

In some implementations described herein, a tailored version of adynamic interactive dialog application is executed with one or morepersona values that are specified by a creator of the tailored versionand/or that are predicted based on structured content and/or other inputprovided by the creator in creating the tailored version. The personavalues can be utilized for one or more of the variables of theinteractive dialog application to thereby also adapt the interactivedialog application based on the persona values. Each of the personavalues can influence audible and/or graphical user interface output thatis generated in execution of the tailored version.

For example, one or more persona values can define the tone, intonation,pitch, and/or other voice characteristics of a computer generated speechto be provided as natural language user interface output in execution ofthe tailored version. Also, for example, one or more persona values candefine term(s), phrase(s), and/or a degree of formality to be utilizedfor various user interface outputs, such as user interface outputsdefined in the fixed code (i.e., those not defined in the specifiedstructured content). For instance, one or more persona values for afirst tailored version can result in various natural language userinterface outputs being provided that are very formal (e.g., excludecolloquialisms and/or other casual utterances), while one or morepersona values for a second tailored version can result in variousnatural language user interface outputs being provided that are verycasual (i.e., a low degree of formality). Also, for instance, one ormore persona values for a first tailored version can result in variousnatural language user interface outputs being provided that includeterms that are specific to a first region (without including termsspecific to a second region), while one or more persona values for asecond tailored version can result in various natural language userinterface outputs being provided that are include terms that arespecific to the second region (without including terms specific to thefirst region). As yet another example, one or more persona values candefine music, sound effects, graphical properties, and/or other featuresthat are provided as user interface output.

Executing a tailored version of a dynamic interactive dialog applicationwith persona values for the tailored version can result in the tailoredversion providing more understandable, and more natural user interfaceoutputs, thereby facilitating more effective communication with a user.For example, techniques described herein can allow a tailored version toconvey meaning to a particular user using language and/or phrasing whichresonates with the user. For instance, as described herein the personavalues can be determined based on the structured content utilized toexecute the tailored version, and resultantly adapted to users that aremore likely to invoke the tailored version. Adaptation of the naturallanguage user interface outputs based on the persona values may make theoverall duration, of an interactive dialog engaged in through executionof the tailored version, shorter than it would otherwise need to be,thereby saving computational load in the computing system(s) executingthe tailored version.

As mentioned above, in various implementations one or more personavalues are predicted based on structured content and/or other inputprovided by the creator in creating the tailored version. The predictedvalues can be automatically assigned to the tailored version and/orpresented to the creator as suggested persona values and, if affirmed bythe creator via user interface input, assigned to the tailored version.In many of those implementations, the persona values are predicted basedon processing, using a trained machine learning model, of at least someof the structured content and/or other input provided in creating thetailored version. For example, at least some of the structured contentcan be applied as at least part of input to the trained machine learningmodel, the input processed using the machine learning model to generateone or more output values, and the persona values selected based on theone or more output values. The trained machine learning model can betrained, for example, based on training instances that are eachgenerated based on previously submitted structured content in generatinga corresponding previous tailored version, and based on previouslysubmitted persona values (e.g., explicitly selected by a correspondingcreator) for the corresponding previous tailored version.

In some implementations described herein, structured content and/orother input provided by a creator in creating a tailored version of aninteractive dialog application is utilized in indexing of the tailoredversion. For example, the tailored version can be indexed based on oneor more invocation phrases provided by a creator for the tailoredversion. As another example, the tailored version can additionally oralternatively be indexed based on one or more entities that aredetermined based on the structured content specified by the creator. Forinstance, at least some of the entities may be determined based onhaving a defined relationship (e.g., in a knowledge graph) to aplurality of entities having aliases included in the structured content.Such entities can be utilized to index the tailored version, even thoughan alias of such entities is not included in the structured content. Forexample, structured content can include aliases for a large quantity ofpoints of interest in a given city, but not include any alias for thegiven city. The aliases for the points of interest, and optionally othercontent, can be utilized to identify, in a knowledge graph, entitiescorresponding to the points of interest. Further, it can be determinedthat all of the entities have a defined relationship (e.g., a “locatedin” relationship), in the knowledge graph, to the given city. Based onthe defined relationship, and based on multiple (e.g., at least athreshold) of the entities having the defined relationship, the tailoredversion can be indexed based on the given city (e.g., indexed by one ormore aliases of the given city). Thereafter, a user can discover thetailored version through submission of user interface input thatreferences the given city. For example, a user can provide spoken input,via an automated assistant interface, of “I want an application about[alias for given city]”. Based on the tailored version being indexedbased on the given city, an automated assistant associated with theautomated assistant interface can automatically execute the tailoredversion—or can cause output to be presented to the user that indicatesthe tailored version as an option for execution, and can execute thetailored version if affirmative user interface input is received inresponse to the presentation. In these and other manners, tailoredversion(s) of dynamic dialog applications that satisfy a request of auser can be efficiently identified and executed. This can prevent a userfrom needing to submit multiple requests to identify such tailoredversions, thereby conserving computational and/or network resources.

In some implementations, a method performed by one or more processors isprovided that includes: receiving, via one or more network interfaces:an indication of a dynamic interactive dialog application, structuredcontent for executing a tailored version of the dynamic interactivedialog application, and at least one invocation phrase for the tailoredversion of the dynamic interactive dialog application. The indication,the structured content, and the at least one invocation phrase aretransmitted in one or more data packets generated by a client device ofa user in response to interaction with the client device by the user.The method further includes the steps of: processing the structuredcontent to automatically select a plurality of persona values for thetailored version of the interactive dialog application, wherein thestructured content does not explicitly indicate the persona values.Subsequent to receiving the indication, the structured content, and theat least one invocation phrase, and subsequent to automaticallyselecting the plurality of persona values, the method includes the stepsof: receiving natural language input provided via an assistant interfaceof the client device or an additional client device of an additionaluser; and determining the natural language input matches the invocationphrase for the tailored version of the interactive dialog application.In response to determining the natural language input matches theinvocation phrase, the method includes executing the tailored version ofthe interactive dialog application, wherein executing the tailoredversion of the interactive dialog application includes generatingmultiple instances of output for presentation via the assistantinterface, each of the multiple instances of output being for acorresponding dialog turn during execution of the interactive dialogapplication and being generated using the structured content and using acorresponding one or more of the persona values.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features.

In various implementations, processing the structured content toautomatically select the plurality of persona values may include:applying, as input to a trained machine learning model, at least some ofthe structured content; processing the at least some of the structuredcontent using the trained machine learning model to generate one or moreoutput values; and selecting the persona values based on the one or moreoutput values. In various implementations, the one or more output valuesmay include a first probability for a first persona and a secondprobability for a second persona, and selecting the persona values basedon the one or more output values may include: selecting the firstpersona over the second persona based on the first probability and thesecond probability; and selecting the persona values based on thepersona values being assigned, in at least one database, to the selectedfirst persona. In other various implementations, the method may furtherinclude: applying, as additional input to the trained machine learningmodel, the indication of the dynamic interactive dialog application; andprocessing, using the trained machine learning model, the indication andthe at least some of the structured content to generate the one or moreoutput values.

In various implementations, processing the structured content toautomatically select the plurality of persona values may include:determining one or more entities based on the structured content;applying, as input to a trained machine learning model, at least some ofthe entities; processing the at least some of the entities using thetrained machine learning model to generate one or more output values;and selecting the persona values based on the one or more output values.

In various implementations, prior to processing the at least some of thestructured content using the trained machine learning model, the methodmay further include: identifying, from one or more databases, multipleprevious user submissions, each of the previous user submissionsincluding previously submitted structured content and correspondingpreviously submitted persona values, the previously submitted personavalues being explicitly selected by a corresponding user; generating aplurality of training instances based on the previous user submissions,each of the training instances being generated based on a correspondingone of the previous user submissions and including training instanceinput that is based on the previously submitted structured content ofthe corresponding one of the previous user submissions and traininginstance output that is based on the previously submitted persona valuesof the corresponding one of the previous user submissions; and trainingthe trained machine learning model based on the plurality of traininginstances. In some of those implementations, the training the machinelearning model may include: processing, using the trained machinelearning model, the training instance input of a given training instanceof the training instances; generating a predicted output based on theprocessing; generating an error based on comparing the predicted outputto the training instance output of the given training instance; andupdating the trained machine learning model based on backpropagationusing the error.

In various implementations, processing the structured content mayinclude parsing the structured content from a document specified by theuser.

In various implementations, wherein the persona values may be related toat least one of tone of the dialog, grammar of the dialog, andnon-verbal sounds provided with the dialog.

In another aspect, a method performed by one or more processors isprovided that includes: receiving, via one or more network interfaces:an indication of a dynamic interactive dialog application, andstructured content for executing a tailored version of the dynamicinteractive dialog application, wherein the indication and thestructured content are transmitted in one or more data packets generatedby a client device of a user in response to interaction with the clientdevice by the user; processing the structured content to determine oneor more related entities; indexing the tailored version of the dynamicinteractive dialog application based on the one or more relatedentities; subsequent to the indexing: receiving natural language inputprovided via an assistant interface of the client device or anadditional client device of an additional user; determining one or moreinvocation entities from the natural language input; identifying amapping of entities, the mapping including at least one of theinvocation entities and at least one of the related entities;identifying the tailored version of the dynamic interactive dialogapplication based on the relationships between the invocation entitiesand the related entities in the mapping. Based on identifying thetailored version of the interactive dialog application, the methodincludes executing a dynamic version of the interactive dialogapplication, wherein executing the dynamic version of the interactivedialog application includes generating multiple instances of output forpresentation via the assistant interface, each of the multiple instancesof output being for a corresponding dialog turn during execution of theinteractive dialog application and being generated using at least someof the structured content of the tailored version of the interactivedialog application.

These and other implementations of technology disclosed herein mayoptionally include one or more of the following features.

In various implementations, processing the structured content todetermine the one or more related entities may include: parsing thestructured content to identify one or more terms; identifying one ormore entities with one or more of the terms as aliases; and determininga given related entity, of the one or more related entities, based onthe given related entity having a defined relationship with multiple ofthe identified one or more entities.

In various implementations, no alias of the given entity may be includedin the structured content. In some of those implementations, determiningthe given related entity is further based on the given related entityhaving the defined relationship with at least a threshold quantity ofthe multiple of the identified one or more entities.

In various implementations, the method may further include: receiving,via one or more processors, at least one invocation phrase for thetailored version of the dynamic interactive dialog application; andfurther indexing the tailored version of the dynamic interactive dialogapplication based on the at least one invocation phrase.

In various implementations, the method may further include: weightingthe related entities based on the structured content and relationshipsbetween the related entities. In some of those implementations,identifying the tailored version may be further based on the weights ofthe related entities. In other implementations, the method may furtherinclude: identifying a second tailored version of the dynamicinteractive dialog application based on the input entities and therelated entities; and selecting the tailored version based on theweights.

In various implementations, the method may further include: identifyinga second tailored version, with second structured content and secondversion related entities, based on the input entities and the secondversion related entities, wherein each of the multiple instances ofoutput are generated using at least some of the structured content andsome of the second structured content.

In addition, some implementations include one or more processors of oneor more computing devices, where the one or more processors are operableto execute instructions stored in associated memory, and where theinstructions are configured to cause performance of any of theaforementioned methods. Some implementations also include one or morenon-transitory computer readable storage media storing computerinstructions executable by one or more processors to perform any of theaforementioned methods.

It should be appreciated that all combinations of the foregoing conceptsand additional concepts described in greater detail herein arecontemplated as being part of the subject matter disclosed herein. Forexample, all combinations of claimed subject matter appearing at the endof this disclosure are contemplated as being part of the subject matterdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an example environment in whichimplementations disclosed herein may be implemented.

FIG. 2 is an example of structured content that may be utilized inimplementations disclosed herein.

FIG. 3 illustrates an example of how persona values may be selected fora request to generate a tailored version of a dynamic interactive dialogapplication.

FIG. 4 is a flowchart illustrating an example method according toimplementations disclosed herein.

FIG. 5 is a flowchart illustrating an example method of generating apersona selection model according to implementations disclosed herein.

FIG. 6 is an illustration of a graph with nodes representing entities ina knowledge graph.

FIG. 7 illustrates an example of indexing a tailored version of anapplication based on entities related to structured content specifiedfor the tailored version of the application.

FIG. 8 illustrates a user, a client device, and an example dialogbetween the user and an automated assistant associated with the clientdevice executing a tailored version of an interactive dialogapplication, according to implementations disclosed herein.

FIG. 9 illustrates a user, a client device, and another example dialogbetween the user and an automated assistant associated with the clientdevice executing another tailored version of the interactive dialogapplication of FIG. 8, according to implementations disclosed herein.

FIG. 10 illustrates an example architecture of a computing device.

DETAILED DESCRIPTION

In some instances, a tailored version of an interactive dialogapplication is generated based on received structured content and areceived indication of the interactive dialog application. Thestructured content and the indication can be transmitted to an automatedassistant, or a component associated with the automated assistant, inresponse to one or more user interface inputs provided by a creator viainteraction with client devices of the creator. The indication of theinteractive dialog application is utilized to identify the interactivedialog application, and the structured content is utilized in executingthe tailored version of the interactive dialog application. Varioustypes of structured content can be provided and utilized in executingthe tailored version of the interactive dialog application. For example,the structured content can be a spreadsheet that includes prompts andpossible responses, such as multiple-choice questions and correspondinganswers (e.g., for each question, a correct answer and one or moreincorrect answers), jokes and corresponding punchlines (e.g., for eachjoke, a corresponding punchline), etc. As another example, a structuredHTML or XML document may be provided, or even an unstructured documentthat is processed and converted to a structured document.

In some implementations, one or more persona values can be assigned tothe tailored version, and the persona values utilized in executing thetailored version. The persona values can indicate: audiblecharacteristics of voice output, grammar characteristics to be used ingenerating the natural language for the voice output, and/or particularterms and/or phrases (e.g., that are in addition to the structuredcontent) to be provided in voice output. For example, the persona valuescan collectively define a discrete persona, such as a queen (e.g.,female voice with proper grammar), a robot (e.g., exaggerated automatedvoice with stiff speaking tone), and/or a teacher. In someimplementations, persona values may be different characteristics of thepresenting voice of the automated assistant which may vary, such as atone value, a grammar value, and a sex value that may be changed tocreate different personas. In some implementations, one or more of thepersona values can be utilized to select, from a plurality of candidatevoice-to-text models, a particular voice-to-text model that conforms tothe persona value(s). In some implementations, one or more of thepersona values can be utilized to select corresponding characteristicsto utilize in voice-to-text conversion.

Turning now to the Figures, FIG. 1 illustrates an example environment inwhich techniques disclosed herein may be implemented. The exampleenvironment includes a client device 106, an automated assistant 110,and a tailored application engine 120. In FIG. 1, the tailoredapplication engine 120 is illustrated as part of the automated assistant110. However, in many implementations the tailored application engine120 may be implemented by one or more components that are separate fromthe automated assistant 110. For example, the tailored applicationengine 120 may interface with the automated assistant 110 over one ormore networks and may optionally interface with the automated assistant110 utilizing one or more application programming interfaces (APIs). Insome implementations where the tailored application engine 120 isseparate from the automated assistant 110, the tailored applicationengine 120 is controlled by a third-party that is unique from a partythat controls the automated assistant 110.

The client device 106 may be, for example, a standalone voice-activatedspeaker device, a desktop computing device, a laptop computing device, atablet computing device, a mobile phone computing device, a computingdevice of a vehicle of the user, and/or a wearable apparatus of the userthat includes a computing device (e.g., a watch of the user having acomputing device, glasses of the user having a computing device, avirtual or augmented reality computing device). Additional and/oralternative client devices may be provided.

Although automated assistant 110 is illustrated in FIG. 1 as separatefrom the client device 106, in some implementations all or aspects ofthe automated assistant 110 may be implemented by the client device 106.For example, in some implementations input processing engine 112 may beimplemented by the client device 106. In implementations where one ormore (e.g., all) aspects of automated assistant 110 are implemented byone or more computing devices remote from the client device 106, theclient device 106 and those aspects of the automated assistant 110communicate via one or more networks, such as a wide area network (WAN)(e.g., the Internet). As described herein, the client device 106 caninclude an automated assistant interface via which a user of the clientdevice 106 interfaces with the automated assistant 110.

Although only one client device 106 is illustrated in combination withthe automated assistant 110, in many implementations the automatedassistant 110 may be remote and may interface with each of a pluralityof client devices of the user, and/or with each of a plurality of clientdevice of multiple users. For example, the automated assistant 110 maymanage communications with each of the multiple devices via differentsessions and may manage multiple sessions in parallel. For instance, theautomated assistant 110 in some implementations may be implemented as acloud-based service employing a cloud infrastructure, e.g., using aserver farm or cluster of high performance computers running softwaresuitable for handling high volumes of requests from multiple users.However, for the sake of simplicity, many examples herein are describedwith respect to a single client device 106.

The automated assistant 110 includes an input processing engine 112, anoutput engine 135, and an invocation engine 160. In someimplementations, one or more of the engines of automated assistant 110may be omitted, combined, and/or implemented in a component that isseparate from automated assistant 110. Moreover, automated assistant 110may include additional engines not illustrated herein for the sake ofsimplicity. For example, automated assistant 110 may include a dialogstate tracking engine, its own dialog engine (or can share the dialogmodule 126 with tailored application engine 120), etc.

The automated assistant 110 receives instances of user input from theclient device 106. For example, the automated assistant 110 may receivefree-form natural language voice input in the form of a streaming audiorecording. The streaming audio recording may be generated by the clientdevice 106 in response to signals received from a microphone of theclient device 106 that captures spoken input of a user of the clientdevice 106. As another example, the automated assistant 110 may receivefree-form natural language typed input. In some implementations, theautomated assistant 110 may receive non-free-form input from a user,such as selection of one of multiple options on a graphical userinterface element—or structured content provided (e.g., in a separatespreadsheet document or other document) in generating of a tailoredversion of an interactive dialog application. In variousimplementations, the input is provided at the client device via anautomated assistant interface via which a user of the client device 106interacts with the automated assistant 110. The interface can be anaudio-only interface, a graphical-only interface, or an audio andgraphical interface.

In some implementations, user input may be generated by the clientdevice 106 and/or provided to the automated assistant 110 in response toan explicit invocation of the automated assistant 110 by a user of theclient device 106. For example, the invocation may be detection by theclient device 106 of certain voice input of the user (e.g., an automatedassistant 110 hot word/phrase such as “Hey Assistant”), user interactionwith a hardware button and/or virtual button (e.g., a tap of a hardwarebutton, a selection of a graphical interface element displayed by theclient device 106), and/or other particular user interface input. Insome implementations, automated assistant 110 may receive user inputthat indicates (directly or indirectly) a particular application that isexecutable (directly or indirectly) by the automated assistant 110. Forexample, input processing engine 112 may receive, from the client device106, input of “Assistant, I want to play my President's quiz”. Inputprocessing engine 112 may parse the received audio and provide theparsed content to invocation engine 160. Invocation engine 160 canutilize the parsed content to determine (e.g., utilizing index 152) that“President's quiz” is an invocation phrase for a tailored version of adynamic interactive dialog application. In response, the invocationengine 160 can transmit an invocation command to tailored applicationengine 120 to cause the tailored application engine 120 to execute thattailored version and engage in an interactive dialog with a user of theclient device 106 via an automated assistant interface.

The automated assistant 110 typically provides an instance of output inresponse to receiving an instance of user input from the client device106. The instance of output may be, for example, audio to be audiblypresented by the device 106 (e.g., output via a speaker of the clientdevice 106), text and/or graphical content to be graphically presentedby the device 106 (e.g., rendered via a display of the client device106), etc. As described herein, when executing a tailored version of aninteractive dialog application, the output provided at a given dialogturn can be generated by tailored application engine 120 based on theinteractive dialog application, and based on structured content for thetailored version and/or persona values for the tailored version. As usedherein, a dialog turn references a user utterance (e.g., an instance ofvoice input or other natural language input) and a responsive systemutterance (e.g., an instance of audible and/or graphical output), orvice versa.

The input processing engine 112 of automated assistant 110 processesnatural language input received via client devices 106 and generatesannotated output for use by one or more other components of theautomated assistant 110, such as invocation engine 160, tailoredapplication engine 120, etc. For example, the input processing engine112 may process natural language free-form input that is generated by auser via one or more user interface input devices of client device 106.The generated annotated output includes one or more annotations of thenatural language input and optionally one or more (e.g., all) of theterms of the natural language input. As another example, the inputprocessing engine 112 may additionally or alternatively include a voiceto text module that receives an instance of voice input (e.g., in theform of digital audio data), and converts the voice input into text thatincludes one or more text words or phrases. In some implementations, thevoice to text module is a streaming voice to text engine. The voice totext module may rely on one or more stored voice-to-text models (alsoreferred to as language models) that each may model a relationshipbetween an audio signal and phonetic units in a language, along withword sequences in the language.

In some implementations, the input processing engine 112 is configuredto identify and annotate various types of grammatical information innatural language input. For example, the input processing engine 112 mayinclude a part of speech tagger configured to annotate terms with theirgrammatical roles. For example, the part of speech tagger may tag eachterm with its part of speech such as “noun,” “verb,” “adjective,”“pronoun,” etc. Also, for example, in some implementations the inputprocessing engine 112 may additionally and/or alternatively include adependency parser configured to determine syntactic relationshipsbetween terms in natural language input. For example, the dependencyparser may determine which terms modify other terms, subjects and verbsof sentences, and so forth (e.g., a parse tree)—and may make annotationsof such dependencies.

The output engine 135 provides instances of output to the client device106. In some situations, an instance of output may be based onresponsive content generated by tailored application engine 120 inexecuting a tailored version of an interactive dialog application. Inother situations, the instance of output may be based on responsivecontent generated by another application that is not necessarily atailored version of an interactive dialog application. For example, theautomated assistant 110 may itself include one or more internalapplications that generate responsive content and/or may interface withthird-party applications that are not tailored version of interactivedialog applications, and that generate responsive content. In someimplementations, the output engine 135 may include a text to speechengine that converts textual components of responsive content to anaudio format, and the output provided by the output engine 135 is in anaudio format (e.g., as streaming audio). In some implementations, theresponsive content may already be in an audio format. In someimplementations, the output engine 135 additionally or alternativelyprovides textual reply content as output (optionally for conversion bythe device 106 to audio) and/or provides other graphical content asoutput for graphical display by the client device 106.

The tailored application engine 120 includes an indexing module 122, apersona module 124, a dialog module 126, an entity module 128, and acontent input engine 130. In some implementations, module(s) of tailoredapplication engine 120 may be omitted, combined, and/or implemented in acomponent that is separate from the tailored application engine 120.Moreover, tailored application engine 120 may include additional modulesnot illustrated herein for the sake of simplicity.

Content input engine 130 processes content provided by a creator forgenerating a tailored version of an interactive dialog application. Insome implementations, the content provided includes structured content.For example, referring to FIG. 2, an example of structured content isprovided. The structured content can be transmitted to the content inputengine 130 from the client device 106, or from another client device(e.g., a client device of another user). The structured content of FIG.2 is a spreadsheet, with each row 205 a-d of the spreadsheet includingan entry in a question column 210, an entry in a correct answer column215, and an entry in each of three incorrect answer columns 220. In someimplementations, the headers of the columns of the spreadsheet of FIG. 2can be prepopulated by the tailored application engine 120 and theentries in each of the rows can be populated by a creator utilizing acorresponding client device and utilizing the headers as guidance. Insome of those implementations, the tailored application engine 120prepopulates the headers based on which, of multiple availableinteractive dialog applications, the creator indicates he/she desires tocreate a tailored version for. For example, the headers of FIG. 2 can beprepopulated based on a creator selecting a “trivia” interactive dialogapplication. On the other hand, if the user selected a “jokes”interactive dialog application, headers of “joke” and “punchline” caninstead be prepopulated.

The content input engine 130 can receive the structured content of FIG.2, optionally process the content, and store the content in tailoredcontent database 158 for utilization in executing a tailored version ofa corresponding interactive dialog application. Processing the contentcan include annotating and/or storing the entries provided by the userbased on the columns and rows for the entries. For example, the contentinput engine 130 can store the entry of column 210, row 205A andannotate it as a “question” entry for the tailored version. Further, thecontent input engine 130 can store the entry of column 215, row 205A,and annotate it as a “correct answer” entry for the preceding stored“question” entry—and can store the entries of columns 220, row 205A, andannotate them as “incorrect answer” entries for the preceding store“question” entry. Processing the content can additionally and/oralternatively include verifying that values of the structured contentconform to one or more required criteria, and prompting the creator tocorrect if not. The one or more required criteria can include a contenttype (e.g., numeric only, alphabetic only), content length (e.g., Xcharacters and/or Y terms), etc.

The content input engine 130 can also store, in association with thestructured content for a tailored version, an indication of thecorresponding interactive dialog application, any provided invocationphrase(s) for the tailored version, and any selected and/or predictedpersona value(s) for the tailored version. Although a spreadsheet isillustrated in FIG. 2, it is understood that content input engine 130can process other types of structured content. Moreover, in someimplementations content input engine 130 can convert non-structuredcontent into a structured format, and then process the structuredformat.

The dialog module 126 executes a tailored version of an interactivedialog application using the application and structured content for thetailored version, optionally along with additional value(s) for thetailored version (e.g., persona value(s)). For example, the dialogmodule 126 can execute a given tailored version of a given interactivedialog application by retrieving fixed code for the given interactivedialog application from applications database 159, and retrievingstructured content and/or other content for the given version fromtailored content database 158. The dialog module 126 can then executethe given tailored version utilizing the fixed code for the giveninteractive dialog application and the tailored content for the tailoredversion.

In executing a tailored version of an interactive dialog application,the dialog module 126 engages in multiple dialog turns. In each dialogturn the dialog module 126 can provide content to output engine 135, andoutput engine 135 can provide the content (or a conversion thereof) asuser interface output to be presented (audibly or graphically) at theclient device 106. The output provided can be based on the interactivedialog application and the structured content and/or persona values.Moreover, the output provided at many dialog turns can be based on userutterance(s) (of the dialog turn and/or prior dialog turn(s)) and/orsystem utterances of prior dialog turns (e.g., a “dialog state”determined based on past user and/or system utterance(s)). Userutterances of the dialog turns can be processed by input processingengine 112, and output from the input processing engine 122 utilized bythe dialog module 126 in determining responsive content to provide.While many instances of output will be based on the interactive dialogapplication and the structured content, it is noted that some instancesof the output can be based on the interactive dialog application withoutreference to the structured content. For example, the interactive dialogapplication can include fixed code that enables response to various userinputs utilizing only the fixed code and/or with reference to othercontent that is not provided by the creator. In other words, while manydialog turns during execution of a tailored version will be influencedby provided structured content, some dialog turns will not.

In some implementations, in executing a tailored version of aninteractive dialog application, the dialog module 126 can additionallyand/or alternatively customize one or more instances of output for agiven user based on performance of the given user. The performance ofthe given user can include performance in one or more earlier dialogturns of the current execution of the tailored version and/orperformance in one or more interactive dialogs with the given user inprior execution(s) of the tailored version, and/or for priorexecution(s) of other tailored versions and/or other interactive dialogapplication(s). For example, if the interactive dialog application is atrivia application, and the given user has struggled to correctly answerquestions, hints can proactively be provided along with questions in oneor more outputs and/or persona values can be adjusted to be more“encouraging”. For example, output provided in response to wrong answersin executing a tailored version of a trivia application can initially be“Wrong, Wrong, Wrong”, but adapted to “Good answer, but notcorrect—please try again” in response to the user incorrectly answeringat least a threshold quantity of questions. Such adaptation can beaccomplished via adaptation of one or more persona values. As anotherexample, output provided in response to correct answers in executing atailored version of a trivia application can initially be “Correct,great work”, but adapted to simply “Correct” (e.g., to speed up thedialog) in response to the user performing well and/or the pace of thedialog slowing down. Performance data of a given user (e.g., score,number of errors, time spent, and/or other data) can be persistedthrough execution of a tailored version, and even across multipleinstances of execution of the tailored version (and/or other tailoredversions and/or other applications), and utilized to customize theexperience of the given user through adaptation of one or more outputs.In some implementations, such user-performance specific adaptation inexecuting a tailored version can be accomplished through adaptation ofpersona value(s), which can include further adaptation of one or morepersona values that are already adapted based on structured content ofthe tailored version and/or other feature(s).

The persona module 124 utilizes the creator provided structured contentof a tailored version and/or other content to select one or more personavalues for a tailored version of an interactive dialog application. Thepersona module 124 can store the persona values in the tailored contentdatabase 158, in association with the tailored version. The selectedpersona values are utilized by dialog module 126 in execution of thetailored version. For example, the selected persona values can beutilized in selecting grammar, tone, and/or other aspects of speechoutput to be provided in one or more dialog turns during execution ofthe tailored version. The persona module 124 may utilize variouscriteria in selecting persona values for a tailored version. Forexample, persona module 124 may utilize the structured content of thetailored version, the invocation phrase(s) of the tailored version,entities associated with the structured content (e.g., as describedbelow with respect to entity module 128), the corresponding interactivedialog application (e.g., is it of the type quiz, joke, list of facts,or transportation scheduler), etc. In some implementations, the personamodule 124 may utilize one or more selection models 156 in selecting oneor more persona values. As described herein, the persona module 124 canautomatically select and implement one or more persona value(s) for atailored version and/or can select one or more persona values andrequire confirmation by the creator prior to implementation for thetailored version.

A persona is a discrete personality and is comprised of a collection ofpersona values, some of which may be predetermined to reflect thatparticular type of persona. For example, personas may include “queen,”“king,” “teacher,” “robot,” and/or one or more other distinct types.Each persona may be represented by a plurality of persona values, eachreflecting a particular aspect of a persona, and each of the discretepersonas may have a preset value for each (or may be limited in thevalues that may be assigned). As an example, a persona may be acollection of persona values and include a speaking voicecharacteristic, a grammar characteristic, and a non-verbal soundcharacteristic (e.g., music played between question rounds of a quiz). A“Queen” persona may have SPEAKING VOICE=(VALUE 1), GRAMMAR=(VALUE 2),and SOUND=(VALUE 3) as persona values. The “Teacher” persona may haveSPEAKING VOICE=(VALUE 4), GRAMMAR=(VALUE 5), and SOUND=(VALUE 6) aspersona values. In some implementations, a persona may be selected for atailored version and the corresponding persona values may be set basedon the persona values that comprise that persona. For example,techniques described herein for selecting persona values may select thepersona values by first selecting a persona, identifying the personavalues that constitute that selected persona, and set the persona valuesof the tailored versions accordingly. In some implementations, one ormore of the persona values may not be part of a persona and may be setindependently of the persona when a persona is selected. For example, a“Teacher” persona may not have a “GENDER” persona value set, and apersona value indicative of “Male” or “Female” may be independentlyassigned to “Teacher” personas.

Entity module 128 utilizes terms and/or other content provided inreceived structured content, to determine one or more entities that arereferenced in the structured content, and optionally one or moreentities that are related to such referenced entities. The entity module128 can utilize entity database 154 (e.g., a knowledge graph) indetermining such entities. For example, structured content can includethe terms “president,” “George Washington,” and “Civil War”. Entitymodule 128 can identify, from entity database 154, entities associatedwith each of the terms. For example, the entity module 128 can identifyan entity associated with the first president of the United States basedon “George Washington” being an alias for that entity. The entity module128 can optionally identify one or more additional entities that arerelated to such referenced entities, such as an entity associated with“U.S. Presidents” based on the “George Washington” having a “belongs tothe group” relationship with a “U.S. Presidents” entity.

The entity module 128 can provide determined entities to indexing module122 and/or persona module 124. Indexing modules 122 can index thetailored version, corresponding to the structured content, based on oneor more entities determined based on the structured content. Personamodule 124 can utilize one or more of the entities in selecting one ormore persona values for the tailored version.

Referring now to FIG. 3, an example is provided of how persona valuescan be selected for a request to generate a tailored version of adynamic interactive dialog application. The content input engine 130receives an indication 171 of an interactive dialog application totailor. This may be, for example, an indication of a quiz application,an indication of a joke application, or an indication of atransportation query application. The indication can be received inresponse to a creator selecting a graphical element that corresponds tothe interactive dialog application, speaking term(s) that correspond tothe interactive dialog application, or otherwise indicating a desire toprovide structured content for the interactive dialog application.Further, the content input engine 130 receives structured content. Forexample, for an indication 171 of a quiz application, the content inputengine 130 may receive a document that includes the content illustratedin FIG. 2. Further still, content input engine 130 receives aninvocation phrase 173. For example, for the structured content of FIG.2, a creator can provide an indication phrase 173 of “PresidentialTrivia.”

Content input engine 130 provides the indication, at least some of thestructured content, and/or the invocation phrase 174 to the personamodule 124 and/or the entity module 128. The entity module 128 utilizesthe indication, at least some of the structured content, and/or theinvocation phrase 174 to identify, using entity database 154, entities175 referenced in one or more of those items, and provides the entities175 to the persona module 124.

The persona module 124 uses at least one of the selection models, andthe data 174 and/or 175, to select one or more persona values 176. Forexample, the persona module 124 can process the data 174 and/or 175utilizing one of the selection models that is a machine learning model,and generate, based on the processing, output that indicates the personavalues 176. For example, the output can indicate a probability for eachof a plurality of discrete personas, one of those personas selectedbased on the probability (e.g., a “Queen” persona), and the personavalues 176 can be a collection of persona values that constitute thatpersona. As another example, the output can include a probability foreach of multiple persona values, and a subset of those persona valuesselected based on the probabilities.

As an example, for structured content that is for an elementary schoolquiz, persona module 124 can select persona value(s) that cause acorresponding tailored version to provide spoken output that is“slower”, to provide less than all possible incorrect answers as optionsfor response to a question, to provide encouraging feedback asresponsive output to even incorrect answers, and/or to limit word usageto terms that would be known to a child. For instance, the selectedpersona values can cause “Close, but not quite! Try again!” to beprovided as responsive content when an incorrect response is received,whereas for structured content that is for an adult audience, personamodule 124 can alternative select persona value(s) that cause output of“Wrong, Wrong, Wrong” to be provided when an incorrect answer isreceived.

In some implementations, persona module 124 can additionally and/oralternatively select persona value(s) based on attribute(s) of a givenuser for which a tailored version is being executed. For example, when atailored version is being executed for a given user that has an “adult”attribute, persona module 124 can select persona value(s) based on suchattribute (and optionally additional attribute(s)). In these and othermanners, persona value(s) of a given tailored version can be adapted ona “per-user” basis, thereby tailoring each version of the tailoredapplication to the user for which it is being executed. Machine learningmodel(s) can optionally be trained and utilized in selection of suchpersona value(s) based on attribute(s) of a user for which a tailoredversion is being executed. For example, the machine learning model(s)can utilize training examples that are based on explicit selections, byusers for which a tailored version is being executed, where the explicitselections indicate one or more persona values that such user(s) desireto be utilized in executing of the tailored version. The trainingexamples can also optionally be based on structured content of thetailored version. For example, the machine learning model(s) can betrained to predict one or more persona values based on attribute(s) of auser for which a tailored version is being executed and based onstructured content and/or other features of the tailored version.

The persona values 176 can be stored in the tailored content database158, along with the structured content 172, the indication 171, and/orthe invocation phrase. Collectively, such values can define a tailoredversion of the interactive dialog application. A subsequent user mayprovide natural language speech to the automated assistant, which thenmay identify terms in the natural language input and identify that theterms correspond to the invocation phrase 173 of the tailored version.For example, the invocation engine 160 (FIG. 1) can process receiveduser interface input to determine which, if any, of a plurality ofpreviously submitted tailored versions of the interactive dialogapplication is being invoked. In some implementations, when a tailoredversion of the application is generated, the creator may provide aninvocation phrase to be utilized in the future to invoke theapplication. In some implementations, one or more entities may beidentified as related to the structured content, and the invocationengine 160 may select one or more of the tailored versions based on userinput and the related entities.

FIG. 4 is a flowchart providing an example of how a tailored version ofan interactive dialog application is executed for a subsequent userbased on structured content and persona values of the tailored version.For convenience, the operations of the flow chart of FIG. 4 aredescribed with reference to a system that performs the operations. Thissystem may include various components of various computer systems.Moreover, while operations of the method of FIG. 4 are shown in aparticular order, this is not meant to be limiting. One or moreoperations may be reordered, omitted or added.

At block 405, natural language input is received from a user. Thenatural language may be received by a component sharing one or morecharacteristics with input processing engine 112 of FIG. 1.

At block 410, one or more terms are identified in the natural languageinput. For example, the term(s) can be identified by a component thatshares one or more characteristics with input processing engine 112.Further, entities associated with one or more of the terms mayoptionally be identified. For example, the entities can be determinedfrom an entity database by a component that shares one or morecharacteristics with entity module 128.

At block 415, a previously generated tailored version, withautomatically selected persona values, is identified based on the termsand/or entities in the natural language input. In some implementations,the previously generated tailored version may be identified based on thenatural language input matching an invocation phrase that is associatedwith the tailored version. In some implementations, the tailored versionmay be identified based on identified relationships between segments ofthe natural language input and one or more entities that are associatedwith the tailored version, as described in greater detail herein.

At block 420, a prompt is generated by dialog module 126. The prompt maythen be provided to the user via the output engine 135. For example,dialog module 126 may generate a text prompt based on the persona valuesand/or the structured content and provide the text to the output engine135, which may convert the text to speech and provide the speech to theuser. When generating prompts, dialog module 126 may vary the grammar,word usage, and/or other characteristics of the prompt based on thepersona values. Further, when providing a speech version of textgenerated by the dialog model 126, output engine 135 may vary the tone,sex, speed of speaking, and/or other characteristics of the outputtedspeech based on one or more of the persona values of the invokedtailored version of the application. In some implementations of block420, the prompt can be a “starting” prompt that is always provided forthe tailored version in an initial iteration.

At block 425, a natural language response of the user is received. Thenatural language response may be analyzed by a component sharingcharacteristics with the input processing engine 112, which then maydetermine one or more terms and/or entities from the input.

At block 430, responsive content is provided. The responsive content canbe generated based on the received input of block 425, the structuredcontent of the tailored version, and the persona value(s) of thetailored version.

After the responsive content is provided, an additional natural languageresponse of the user can be received at another iteration of block 425,and additional responsive content again generated and provided atanother iteration of block 430. This may continue until the tailoredversion is completed, an instance of a natural language response atblock 425 indicates a desire to cease interaction with the tailoredversion, and/or other condition(s) are satisfied.

FIG. 5 is a flowchart illustrating another example method according toimplementations disclosed herein. FIG. 5 illustrates an example oftraining a machine learning model (e.g., a neural network model) forutilization in selecting persona values. For convenience, the operationsof the flow chart of FIG. 5 are described with reference to a systemthat performs the operations. This system may include various componentsof various computer systems. Moreover, while operations of the method ofFIG. 5 are shown in a particular order, this is not meant to belimiting. One or more operations may be reordered, omitted or added.

At block 552, the system selects the structured content and personavalues of a tailored version of an interactive dialog application. Asone example, the structured content and persona values may be selectedfrom database 158, and the persona values may have been explicitlyindicated by a corresponding creator and/or confirmed as desired personavalues by the corresponding creator.

At block 554, the system generates a training instance based on thestructured content and the persona values. Block 554 includes sub-blocks5541 and 5542.

At sub-block 5541, the system generates training instance input of thetraining instance based on the structured content and optionally basedon an indication of the interactive dialog application to which thetailored version corresponds. In some implementations, the systemadditionally or alternatively generates training instance input of thetraining instance based on entities determined based on the structuredcontent. As one example, the training instance input can include anindication of the interactive dialog application, and a subset of termsfrom the structured content, such as a title and the first X terms, orthe X most frequently occurring terms. For instance, the traininginstance input can include the 50 terms of the structured content withthe highest TFIDF values, along with a value that indicates theinteractive dialog application. As another example, the traininginstance input can include an indication of the interactive dialogapplication, as well as an embedding of some (or all) of the terms(and/or other content) of the structured content. For example, theembedding of the terms of the structured content can be a Word2Vecembedding generated utilizing a separate model.

At sub-block 5542, the system generates training instance output of thetraining instance based on the persona values. For example, the traininginstance output can include X outputs, each representing a discretepersona. For a given training instance, the training instance output caninclude a “1” (or other “positive” value) for the output correspondingto the discrete persona to which the persona values of block 552conform, and a “0” (or other “negative” value) for all other outputs. Asanother example, the training instance output can include Y outputs,each representing a persona characteristic. For a given traininginstance, the training instance output can include, for each of the Youtputs, a value indicating the persona value, of persona values ofblock 552, for the persona characteristic that is represented by theoutput. For instance, one of the Y outputs can indicate a degree of“formalism”, and the training instance output for that output can be “0”(or other value) if the corresponding persona value of block 552 is“informal”, and a “1” (or other value) if the corresponding personavalue of block 552 is “formal”.

At block 556, the system determines whether there are additionaltailored versions of interactive dialog applications to process. If so,the system repeats blocks 552 and 554 using structured content andpersona values from an additional tailored version.

Blocks 558-566 may be performed following, or in parallel with, multipleiterations of blocks 552, 554, and 556.

At block 558, the system selects a training instance generated in aniteration of block 554.

At block 560, the system applies the training instance as input to amachine learning model. For example, the machine learning model can haveinput dimensions that correspond to the dimensions of the traininginstance input generated at block 5541.

At block 562, the system generates output over the machine learningmodel based on the applied training instance input. For example, themachine learning model can have output dimensions that correspond to thedimensions of the training instance output generated at block 5541(e.g., each dimension of the output can correspond to a personacharacteristic).

At block 564, the system updates the machine learning model based on thegenerated output and the training instance output. For example, thesystem can determine an error based on the output generated at block 562and the training instance output, and backpropagate the error over themachine learning model.

At block 566, the system determines whether there are one or moreadditional unprocessed training instances. If so, the system proceedsback to block 558, selects an additional training instance, thenperforms blocks 560, 562, and 564 based on the additional unprocessedtraining instance. In some implementations, at block 566 the system maydetermine not to process any additional unprocessed training instancesif one or more training criteria have been satisfied (e.g., a thresholdnumber of epochs have occurred and/or a threshold duration of traininghas occurred). Although method 500 is described with respect to anon-batch learning technique, batch learning may additionally and/oralternatively be utilized.

A machine learning model trained according to the method of FIG. 5 canthereafter be utilized to predict, based on structured content and/orother content indicated by a creator of a tailored version of aninteractive dialog application, persona values for the tailored version.For example, the structured content of FIG. 2 may be provided as inputto the model, and a personality persona parameter may be a value of“Teacher” with a probability of 0.8 and “Queen” with a probability of0.2. This may indicate that, based on the structured content, it is morelikely that the user would have interest in the quiz application beingprovided with a teacher personality than a queen personality.

FIG. 5 is described with respect to one example of a persona selectionmodel that can be generated and utilized. However, additional and/oralternative persona selection models can be utilized in selecting one ormore persona values, such as alternatives described herein. Suchadditional and/or alternative persona selections models may optionallybe machine learning models trained based on training instances that varyfrom those described with respect to FIG. 5.

As one example, a selection model can be generated based on pastexplicit selections of persona values by various users and such aselection model may additionally or alternatively be utilized inselecting a particular persona value. For instance, in someimplementations, indications of multiple persona values may be presentedto a user and a user selection of a single persona value of the multiplepersona values can be utilized to select the single persona value fromthe multiple values. Such explicit selections of multiple users can beutilized to generate a selection model. For example, training instancescan be generated that are similar to those described above, but thetraining instance output of each training instance can be generatedbased on the persona value selected by the user. For instance, for atraining instance a “1” (or other “positive value”) can be utilized forthe output dimension corresponding to the selected personality personavalue (such as a “Teacher” personality) and a “0” (or other “negative”value) can be utilized for each of the output dimensions that correspondto all other persona values. Also, for instance, for a training instancea “1” (or other “positive value”) can be utilized for the outputdimension corresponding to the selected persona value, a “0.5” (or other“intermediate value”) can be utilized for the output dimension(s)corresponding to the other persona value(s) presented to the user butnot selected, and a “0” (or other “negative” value) can be utilized foreach of the output dimensions that correspond to all other personavalues. In this and other manners, explicit selections of persona valuesby users can be leveraged in generating one or more persona selectionmodels.

As mentioned with respect to FIG. 1, indexing module 122 receivesstructured content from a user and indexes, in index 152, acorresponding tailored version based on one or more entities that arerelated to the structured content. After the tailored version of theapplication has been stored with indications of the related entities,natural language input of a subsequent user may be parsed for terms andentities related to the parsed terms may be identified in entitydatabase 154 by entity module 128. By allowing flexibility in indexinguser-created applications, the user and/or subsequent users are notrequired to know an exact invocation phrase. Instead, the indexingmodule 122 allows a user to “discover” the content by providing naturallanguage input that indicates, for example, the desired subject matterof served content.

As a working example, a user may provide structured content for a quizapplication that includes questions regarding state capitals. Thus, theanswers (both correct and incorrect) may be names of cities, with thequestions each including a state name (or vice versa). The structuredcontent may be received by the content input engine 130, as previouslydescribed. Further, the structured content may be provided with anindication of a dynamic dialog application and optionally with aninvocation phrase to invoke the content in a tailored version of theapplication in the future. After parsing the structure content, contentinput engine 130 may provide entity module 128 with the parsed content,which then may identify one or more related entities in entity database154. Going back to the working example and referring to FIG. 6, a graphof a plurality of nodes is provided. Each of the nodes includes an aliasfor an entity and is representative of a portion of the entity database154. The nodes include state capitals, including “Sacramento” 610,“Columbus” 645, “Albany” 640, and “Olympia” 635. Further, the graphincludes nodes that represent related entities. For example, all of thestate capital nodes are connected to a “state capital cities” node 625.

When structured content related to a state capital quiz application isreceived, entity module 128 may identify nodes in the graph related tothe structured content. For example, the structured content may includea questions prompt of “What is the capital of California?” with possibleanswers of “Sacramento” and “Los Angeles.” The corresponding nodes ofthe graph may then be identified. Entity module 128 may then provideindexing module 122 with indications of the corresponding nodes and/orwith indications of entities that are related to the nodes. For example,the state capital quiz may additionally include the question “What isthe capital of New York?,” with an answer choice of “Albany,” and entitymodule 128 may identify the node for “State Capital Cities” as a generalcategory linking the nodes for “Sacramento” 610 and “Albany” 640.

In some implementations, entity module 128 may only identify nodesrelated to some of the structured content. For example, in a quizapplication, entity module 128 may only identify nodes that are relatedto correct answers and not to incorrect answers to avoid associatingincorrect entities with the structured content. In some implementations,entity module 128 may further identify entities that are related to theinvocation phrase, if provided by the user. For example, the user mayprovide the invocation phrase of “Capital Cities” and entity module 128may identify “State Capital Cities” 625.

In some implementations, relationships between the structured contentand one or more of the entities may be weighted. For example, entitymodule 128 may assign a weight to entities identified from correctanswers in a quiz application with a score that is more indicative ofrelated than entities that are related to incorrect answers in thestructured content. Further, entity module 128 may weigh relationshipsto categories or other entities based on the number of entities that arerelated both to the structured content and the entity. For example, forstructured content that includes “Sacramento” 610, “Olympia” 635, and“Albany” 640, entity module 128 may weigh the relationship to “StateCapital Cities” 625 higher than “Western U.S. Cities” 630 because moreof the entities that are related to the structured content are relatedto “State Capital Cities” 625.

Indexing module 122 then indexes the tailored version of the applicationwith one or more of the entities. In some embodiments, indexing module122 may index the tailored version of the application with allidentified entities. In some implementations, indexing module 122 mayindex the tailored version with only those entities with relation scoresthat exceed a threshold. In some implementations, indexing module 122may utilize one or more training models to determine which of theentities to use in indexing of the tailored version of the application.

Input processing engine 112 may receive natural language input from auser and identify a tailored version of an interactive dialogapplication to provide to the user based on entities that are indexedwith the tailored version. Referring to FIG. 7, natural language input181 is received by input processing engine 112, as previously described.Input processing engine 112 parses the input to identify one or moreterms in the input. For example, a user may speak the phrase “Give me astate capital quiz” and input processing engine 112 may identify termsof “State,” “State Capital,” and “Quiz” as parsed input 181. Some of theparsed input 181 may be provided to entity module 128, which thenidentifies one or more related entities 183 in an entity database. Insome implementations, entity module 128 may assign weights to theidentified entities based on the number of associations between theparsed input 181 and the identified entities.

Indexing module 122 receives the related entities 183 (and associatedweights, if assigned) and identifies one or more tailored versions ofapplications 184 that are indexed by entities that are included in therelated entities 183. For example, entity module 128 may identify “StateCapital Cities” as an alias for a related entity, and indexing module122 may identify the example tailored version as the version to provideto the user. In some implementations, indexing module 122 may identifymultiple potential versions of the application and select one of theversions based on the weights assigned to the related entities 183 byentity module 128.

In some implementations, indexing module 122 may identify multiplepotential versions and may provide the user with a version that includescontent from the multiple potential versions. For example, indexingmodule 122 may identify the “State Capital City” quiz application andmay further identify a second “State Capital City” quiz in the tailoredcontent database 158 based on the entities associated with the secondversion and the related entities. Indexing module 122 can optionallyutilize index 122 (FIG. 1) in such identification. Thus, the user may beprovided with a hybrid application that includes structured content frommultiple sources that is seamlessly presented as a single version, evenif unrelated users created the two versions.

FIG. 8 illustrates a user 101, a voice-enabled client device 806, and anexample of dialog that may occur between the user 101 and an automatedassistant associated with the client device 806 with access to atailored version of the interactive dialog application. The clientdevice 806 includes one or more microphones and one or more speakers.One or more aspects of the automated assistant 110 of FIG. 1 may beimplemented on the client device 806 and/or on one or more computingdevices that are in network communication with the client device 806.Accordingly, for ease in explanation the automated assistant 110 isreferenced in description of FIG. 8.

User input 880A is an invocation phrase for a tailored version of adynamic interactive quiz application. The input is received by inputprocessing engine 112, which identifies “quiz” as pertaining to atailored application. Thus, input processing engine 112 providesinvocation engine 160 with the parsed input, as described above. In someimplementations, invocation engine 160 may determine that the input doesnot contain an explicit invocation phrase and may provide the indexingmodule 122 with the input to determine one or more input entities andidentify versions indexed by related entity that may be invoked by theprovided input.

At output 882A, a prompt is provided. The prompt is provided in a“Teacher” persona and addresses the user as a student in a “class.” Atoutput 882B, a non-verbal sound (i.e., a bell ringing) is included inthe prompt and may additionally be part of the “Teacher” persona and/orrelated to one or more persona values. The prompt further includesstructured content in the form of a question.

At user input 880B, the user provides an answer. Input processing engine112 parses the input and provides the parsed input to dialog module 126.Dialog module 126 verifies that the input is correct (i.e., matches thecorrect answer in the structured content) and generates a new dialogturn to provide to the user. Further, output 882C includes structuredcontent as well as dialog generated based on the persona or personavalues associated with the version of the application. The user respondsincorrectly at user input 880C and the next output 882D generated bydialog module 126 admonishes the incorrect answer. As an alternativeexample, if the structured content had indicated that the quiz was morelikely for a young child, output 882D may have provided more encouragingwords, allowed a second guess, and/or provided a hint to the userinstead of the dialog shown in FIG. 8. At user input 880F, the userindicates that a desire to end the application. This may be a standardinvocation phrase and/or one of several phrases that indicates to theautomated assistant to stop sending input to tailored application.

FIG. 9 illustrates a user 101, a voice-enabled client device 806, andanother example of dialog that may occur between the user 101 and anautomated assistant associated with the client device 806 with access toa tailored version of the interactive dialog application with one ormore persona values that are different from the persona values of thedialog of FIG. 8, but with the same structured content. At user input980A, the user invokes the tailored version in the same manner as thedialog in FIG. 8.

At output 982A, a prompt is provided. In this dialog, the tailoredversion is instead associated with a “Teacher” persona and addresses theuser as a “subject” as opposed as “class” in the previous example.Dialog module 126 may identify that a title for the user is required atthis output and determine, based on the persona values associated withthe version of the application, that the “Queen” persona utilizes“subject” as a name for the user. At output 982B, a different non-verbalsound (i.e., trumpets) is included in the prompt and may additionally bepart of the “Queen” persona and/or related to one or more personavalues. Dialog module 126 may insert a different sound in the promptsdepending on one or more of the associated persona values. The promptfurther includes the same structured content in the form of a question.

At user input 980B, the user provides an answer. It is the same answeras previously provided at this user input step in FIG. 8 and dialogmodule 126 handles the response in the same manner. Further, output 982Cincludes structured content as well as dialog generated based on thepersona or persona values associated with the version of theapplication, again tailored to match one or more of the persona valuesselected for the tailored version. The user responds incorrectly at userinput 980C and the next output 982D generated by dialog module 126admonishes the incorrect answer, though using different terms than inFIG. 8. At user input 980F, the user indicates that a desire to end theapplication. This may be a standard invocation phrase and/or one ofseveral phrases that indicates to the automated assistant to stopsending input to tailored application.

Although some examples described above are described with respect to atrivia interactive dialog application, it is understood that variousimplementations can be utilized with various types of interactive dialogapplications. For example, in some implementations provided structuredcontent can be a bus time table, a train timetable, or othertransportation timetable. For instance, the structured content can be abus time table that includes a plurality of stops (e.g., intersections)and times for each of those stops. The interactive dialog applicationcan include fixed code that enables responses to various queries in aconversational manner. In executing a tailored version that is based onthe bus time table, the interactive dialog application can utilize thefixed code in determining what types of responses to provide in responseto various queries, and utilize the structured content in determining atleast some of the content to provide in various responses to variousqueries.

FIG. 10 is a block diagram of an example computing device 1010 that mayoptionally be utilized to perform one or more aspects of techniquesdescribed herein. In some implementations, one or more of device 106,automated assistant 110, and/or other component(s) may comprise one ormore components of the example computing device 1010.

Computing device 1010 typically includes at least one processor 1014which communicates with a number of peripheral devices via bus subsystem1012. These peripheral devices may include a storage subsystem 1024,including, for example, a memory subsystem 1025 and a file storagesubsystem 1026, user interface output devices 1020, user interface inputdevices 1022, and a network interface subsystem 1016. The input andoutput devices allow user interaction with computing device 1010.Network interface subsystem 1016 provides an interface to outsidenetworks and is coupled to corresponding interface devices in othercomputing devices.

User interface input devices 1022 may include a keyboard, pointingdevices such as a mouse, trackball, touchpad, or graphics tablet, ascanner, a touchscreen incorporated into the display, audio inputdevices such as voice recognition systems, microphones, and/or othertypes of input devices. In general, use of the term “input device” isintended to include all possible types of devices and ways to inputinformation into computing device 1010 or onto a communication network.

User interface output devices 1020 may include a display subsystem, aprinter, a fax machine, or non-visual displays such as audio outputdevices. The display subsystem may include a cathode ray tube (CRT), aflat-panel device such as a liquid crystal display (LCD), a projectiondevice, or some other mechanism for creating a visible image. Thedisplay subsystem may also provide non-visual display such as via audiooutput devices. In general, use of the term “output device” is intendedto include all possible types of devices and ways to output informationfrom computing device 1010 to the user or to another machine orcomputing device.

Storage subsystem 1024 stores programming and data constructs thatprovide the functionality of some or all of the modules describedherein. For example, the storage subsystem 1024 may include the logic toperform selected aspects of various methods described herein.

These software modules are generally executed by processor 1014 alone orin combination with other processors. Memory 1025 used in the storagesubsystem 1024 can include a number of memories including a main randomaccess memory (RAM) 1130 for storage of instructions and data duringprogram execution and a read only memory (ROM) 1032 in which fixedinstructions are stored. A file storage subsystem 1026 can providepersistent storage for program and data files, and may include a harddisk drive, a floppy disk drive along with associated removable media, aCD-ROM drive, an optical drive, or removable media cartridges. Themodules implementing the functionality of certain implementations may bestored by file storage subsystem 1026 in the storage subsystem 1124, orin other machines accessible by the processor(s) 1014.

Bus subsystem 1012 provides a mechanism for letting the variouscomponents and subsystems of computing device 1010 communicate with eachother as intended. Although bus subsystem 1012 is shown schematically asa single bus, alternative implementations of the bus subsystem may usemultiple busses.

Computing device 1010 can be of varying types including a workstation,server, computing cluster, blade server, server farm, or any other dataprocessing system or computing device. Due to the ever-changing natureof computers and networks, the description of computing device 1010depicted in FIG. 10 is intended only as a specific example for purposesof illustrating some implementations. Many other configurations ofcomputing device 1010 are possible having more or fewer components thanthe computing device depicted in FIG. 10.

In situations in which certain implementations discussed herein maycollect or use personal information about users (e.g., user dataextracted from other electronic communications, information about auser's social network, a user's location, a user's time, a user'sbiometric information, and a user's activities and demographicinformation), users are provided with one or more opportunities tocontrol whether information is collected, whether the personalinformation is stored, whether the personal information is used, and howthe information is collected about the user, stored and used. That is,implementations of the systems and methods discussed herein collect,store and/or use user personal information only upon receiving explicitauthorization from the relevant users to do so. For example, a user isprovided with control over whether programs or features collect userinformation about that particular user or other users relevant to theprogram or feature. Each user for which personal information is to becollected is presented with one or more options to allow control overthe information collection relevant to that user, to provide permissionor authorization as to whether the information is collected and as towhich portions of the information are to be collected. For example,users can be provided with one or more such control options over acommunication network. In addition, certain data may be treated in oneor more ways before it is stored or used so that personally identifiableinformation is removed. As one example, a user's identity may be treatedso that no personally identifiable information can be determined. Asanother example, a user's geographic location may be generalized to alarger region so that the user's particular location cannot bedetermined.

1. A method implemented by one or more processors, comprising:receiving, via one or more network interfaces: an indication of adynamic interactive dialog application, structured content for executinga tailored version of the dynamic interactive dialog application, and atleast one invocation phrase for the tailored version of the dynamicinteractive dialog application, wherein the indication, the structuredcontent, and the at least one invocation phrase are transmitted in oneor more data packets generated by a client device of a user in responseto interaction with the client device by the user; processing one, orboth of: the indication, and the structured content, to automaticallyselect a plurality of persona values for the tailored version of theinteractive dialog application, wherein the structured content does notexplicitly indicate the persona values, and wherein the persona valuesinclude particular terms or phrases to be provided during execution ofthe tailored version of the interactive dialog application, and includenon-verbal sounds to be provided during execution of the tailoredversion of the interactive dialog application; subsequent to receivingthe indication, the structured content, and the at least one invocationphrase, and subsequent to automatically selecting the plurality ofpersona values: receiving natural language input provided via anassistant interface of the client device or an additional client deviceof an additional user; determining the natural language input matchesthe invocation phrase for the tailored version of the interactive dialogapplication; in response to determining the natural language inputmatches the invocation phrase: executing the tailored version of theinteractive dialog application, wherein executing the tailored versionof the interactive dialog application comprises generating multipleinstances of output for presentation via the assistant interface, eachof the multiple instances of output being for a corresponding dialogturn during execution of the interactive dialog application and beinggenerated using the structured content and using a corresponding one ormore of the persona values.
 2. The method of claim 1, wherein processingone, or both, of the indication and the structured content toautomatically select the plurality of persona values comprises:applying, as input to a trained machine learning model, one or both ofthe indication and at least some of the structured content; processingthe input using the trained machine learning model to generate one ormore output values; and selecting the persona values based on the one ormore output values.
 3. The method of claim 2, wherein the one or moreoutput values comprise a first probability for a first persona and asecond probability for a second persona, and wherein selecting thepersona values based on the one or more output values comprises:selecting the first persona over the second persona based on the firstprobability and the second probability; and selecting the persona valuesbased on the persona values being assigned, in at least one database, tothe selected first persona.
 4. The method of claim 2, furthercomprising, prior to processing one, or both, of the indication and thestructured content using the trained machine learning model:identifying, from one or more databases, multiple previous usersubmissions, each of the previous user submissions including previouslysubmitted structured content and corresponding previously submittedpersona values, the previously submitted persona values being explicitlyselected by a corresponding user; generating a plurality of traininginstances based on the previous user submissions, each of the traininginstances being generated based on a corresponding one of the previoususer submissions and including training instance input that is based onthe previously submitted structured content of the corresponding one ofthe previous user submissions and training instance output that is basedon the previously submitted persona values of the corresponding one ofthe previous user submissions; and training the trained machine learningmodel based on the plurality of training instances.
 5. The method ofclaim 4, wherein training the machine learning model comprises:processing, using the trained machine learning model, the traininginstance input of a given training instance of the training instances;generating a predicted output based on the processing; generating anerror based on comparing the predicted output to the training instanceoutput of the given training instance; and updating the trained machinelearning model based on backpropagation using the error.
 6. The methodof claim 1, wherein processing structured content to automaticallyselect the plurality of persona values comprises: determining one ormore entities based on the structured content; applying, as input to atrained machine learning model, at least some of the entities;processing the at least some of the entities using the trained machinelearning model to generate one or more output values; and selecting thepersona values based on the one or more output values.
 7. The method ofclaim 1, wherein processing the structured content includes parsing thestructured content from a document specified by the user.
 8. The methodof claim 1, further comprising, subsequent to automatically selectingthe plurality of persona values but prior to receiving the naturallanguage input: prompting the user to confirm the automatically selectedplurality of persona values to be used during execution of the tailoredversion of the dynamic interactive dialog application; and receiving,via the client device of the user, the confirmation of the automaticallyselected plurality of persona values to be used during the execution ofthe tailored version of the dynamic interactive dialog application.
 9. Asystem of one or more computing devices, the system comprising: memorystoring instructions; one or more processors operable to executeinstructions stored in the memory to cause the one or more processorsto: receive, in one or more data packets generated by a client device ofa user in response to interaction with the client device by the user: anindication of a dynamic interactive dialog application, structuredcontent for executing a tailored version of the dynamic interactivedialog application, and at least one invocation phrase for the tailoredversion of the dynamic interactive dialog application; process one, orboth of: the indication, and the structured content, to automaticallyselect a plurality of persona values for the tailored version of theinteractive dialog application, wherein the structured content does notexplicitly indicate the persona values, and wherein the persona valuesinclude particular terms or phrases to be provided during execution ofthe tailored version of the interactive dialog application, and includenon-verbal sounds to be provided during execution of the tailoredversion of the interactive dialog application; subsequent to receivingthe indication, the structured content, and the at least one invocationphrase, and subsequent to automatically selecting the plurality ofpersona values: receive natural language input provided via an assistantinterface of the client device or an additional client device of anadditional user; determine the natural language input matches theinvocation phrase for the tailored version of the interactive dialogapplication; in response to determining the natural language inputmatches the invocation phrase: execute the tailored version of theinteractive dialog application, wherein in executing the tailoredversion of the interactive dialog application one or more of theprocessors generate multiple instances of output for presentation viathe assistant interface, each of the multiple instances of output beingfor a corresponding dialog turn during execution of the interactivedialog application and being generated using the structured content andusing a corresponding one or more of the persona values.
 10. The systemof claim 9, wherein in processing one, or both, of the indication andthe structured content to automatically select the plurality of personavalue, one or more of the processors are to: apply, as input to atrained machine learning model, one or both of the indication and atleast some of the structured content; process the input using thetrained machine learning model to generate one or more output values;and select the persona values based on the one or more output values.11. The system of claim 10, wherein the one or more output valuescomprise a first probability for a first persona and a secondprobability for a second persona, and wherein in selecting the personavalues based on the one or more output values one or more of theprocessors are to: select the first persona over the second personabased on the first probability and the second probability; and selectthe persona values based on the persona values being assigned, in atleast one database, to the selected first persona.
 12. The system ofclaim 11, wherein the instructions stored in the memory further causeone or more of the processors to, prior to processing one, or both, ofthe indication and the structured content using the trained machinelearning model: identify, from one or more databases, multiple previoususer submissions, each of the previous user submissions includingpreviously submitted structured content and corresponding previouslysubmitted persona values, the previously submitted persona values beingexplicitly selected by a corresponding user; generate a plurality oftraining instances based on the previous user submissions, each of thetraining instances being generated based on a corresponding one of theprevious user submissions and including training instance input that isbased on the previously submitted structured content of thecorresponding one of the previous user submissions and training instanceoutput that is based on the previously submitted persona values of thecorresponding one of the previous user submissions; and train thetrained machine learning model based on the plurality of traininginstances.
 13. The system of claim 12, wherein in training the machinelearning model one or more of the processors are to: process, using thetrained machine learning model, the training instance input of a giventraining instance of the training instances; generate a predicted outputbased on the processing; generate an error based on comparing thepredicted output to the training instance output of the given traininginstance; and update the trained machine learning model based onbackpropagation using the error.
 14. The system of claim 9, wherein inprocessing structured content to automatically select the plurality ofpersona values one or more of the processors are to: determine one ormore entities based on the structured content; apply, as input to atrained machine learning model, at least some of the entities; processthe at least some of the entities using the trained machine learningmodel to generate one or more output values; and select the personavalues based on the one or more output values.
 15. The system of claim9, wherein in processing the structured content one or more of theprocessors are to parse the structured content from a document specifiedby the user.
 16. The system of claim 9, wherein the instructions storedin the memory further cause one or more of the processors to, subsequentto automatically selecting the plurality of persona values but prior toreceiving the natural language input: prompt the user to confirm theautomatically selected plurality of persona values to be used duringexecution of the tailored version of the dynamic interactive dialogapplication; and receive, via the client device of the user, theconfirmation of the automatically selected plurality of persona valuesto be used during the execution of the tailored version of the dynamicinteractive dialog application.